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Record W4410632616 · doi:10.22215/etd/2025-16452

Design and Implementation of a High-Performance SAR ADC with Non-Standard Capacitor Arrays and Optimized Switching Control Logic

2025· dissertation· en· W4410632616 on OpenAlexaff

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsCarleton University
Fundersnot available
KeywordsSuccessive approximation ADCCapacitorComputer scienceElectronic engineeringControl (management)Electrical engineeringEngineeringArtificial intelligenceVoltage

Abstract

fetched live from OpenAlex

The results in this thesis show the design of a SAR ADC made in a 130nm PDK with a non-standard capacitor array to have a smaller total layout area and higher ENOB while operating at a sampling rate of 10kS/s. Its design includes adding a clock-boosting circuit to the sample and hold circuit with an ENOB of 14.1, a VCDE to allow for higher sampled voltages to be converted to digital bit streams. The TSMC 130nm PDK was also used to for their standard cell library to create the SAR logic block to minimize the size of its total layout area. Schematic-level simulations of the SAR ADC report an ENOB of 9.5, a best-case DNL and INL of +0.65/-0.65 and +0.97/-0.96, respectively, average power consumption of 1.82μW, and a figure of merit (FOM) of 226.5 fJ/Conv after fixing a power consumption issue in the comparator with calibration schema’s SR latch.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.804
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.218
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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